Overview

Dataset statistics

Number of variables29
Number of observations54
Missing cells71
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.4 KiB
Average record size in memory234.4 B

Variable types

Numeric9
Categorical20

Alerts

type has constant value "regular" Constant
airdate has constant value "2020-12-05" Constant
_embedded_show_dvdCountry has constant value "nan" Constant
url has a high cardinality: 54 distinct values High cardinality
name has a high cardinality: 51 distinct values High cardinality
_links_self_href has a high cardinality: 54 distinct values High cardinality
season is highly correlated with _embedded_show_updatedHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_updated is highly correlated with seasonHigh correlation
season is highly correlated with runtime and 2 other fieldsHigh correlation
runtime is highly correlated with season and 2 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with season and 2 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with season and 2 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with _embedded_show_summary and 15 other fieldsHigh correlation
summary is highly correlated with url and 6 other fieldsHigh correlation
_embedded_show_summary is highly correlated with _embedded_show_officialSite and 15 other fieldsHigh correlation
_embedded_show_type is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
_embedded_show_status is highly correlated with _embedded_show_type and 8 other fieldsHigh correlation
url is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
image is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
_embedded_show_ended is highly correlated with _embedded_show_officialSite and 12 other fieldsHigh correlation
_embedded_show_name is highly correlated with _embedded_show_officialSite and 15 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with _embedded_show_officialSite and 15 other fieldsHigh correlation
name is highly correlated with url and 5 other fieldsHigh correlation
airdate is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
airtime is highly correlated with _embedded_show_officialSite and 12 other fieldsHigh correlation
_links_self_href is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
_embedded_show_genres is highly correlated with _embedded_show_officialSite and 14 other fieldsHigh correlation
type is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
_embedded_show_url is highly correlated with _embedded_show_officialSite and 15 other fieldsHigh correlation
_embedded_show_language is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
airstamp is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
id is highly correlated with url and 15 other fieldsHigh correlation
url is highly correlated with id and 24 other fieldsHigh correlation
name is highly correlated with id and 17 other fieldsHigh correlation
season is highly correlated with url and 15 other fieldsHigh correlation
number is highly correlated with url and 12 other fieldsHigh correlation
airtime is highly correlated with url and 18 other fieldsHigh correlation
airstamp is highly correlated with id and 23 other fieldsHigh correlation
runtime is highly correlated with url and 14 other fieldsHigh correlation
image is highly correlated with id and 23 other fieldsHigh correlation
summary is highly correlated with url and 13 other fieldsHigh correlation
_embedded_show_id is highly correlated with id and 18 other fieldsHigh correlation
_embedded_show_url is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_name is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_type is highly correlated with id and 20 other fieldsHigh correlation
_embedded_show_language is highly correlated with id and 15 other fieldsHigh correlation
_embedded_show_genres is highly correlated with id and 17 other fieldsHigh correlation
_embedded_show_status is highly correlated with id and 16 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with url and 14 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with url and 16 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with id and 23 other fieldsHigh correlation
_embedded_show_ended is highly correlated with url and 15 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_weight is highly correlated with url and 17 other fieldsHigh correlation
_embedded_show_summary is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_updated is highly correlated with id and 19 other fieldsHigh correlation
_links_self_href is highly correlated with id and 24 other fieldsHigh correlation
runtime has 5 (9.3%) missing values Missing
image has 37 (68.5%) missing values Missing
_embedded_show_runtime has 24 (44.4%) missing values Missing
_embedded_show_averageRuntime has 5 (9.3%) missing values Missing
url is uniformly distributed Uniform
name is uniformly distributed Uniform
image is uniformly distributed Uniform
_links_self_href is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links_self_href has unique values Unique
_embedded_show_weight has 1 (1.9%) zeros Zeros

Reproduction

Analysis started2022-05-10 02:02:11.337747
Analysis finished2022-05-10 02:02:41.791425
Duration30.45 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2060896.685
Minimum1943279
Maximum2318097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-05-09T21:02:41.889143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1943279
5-th percentile1958661.35
Q11972882.25
median2010852
Q32153995.75
95-th percentile2239169.25
Maximum2318097
Range374818
Interquartile range (IQR)181113.5

Descriptive statistics

Standard deviation104708.4349
Coefficient of variation (CV)0.05080722176
Kurtosis-0.5467811051
Mean2060896.685
Median Absolute Deviation (MAD)46863.5
Skewness0.7294637316
Sum111288421
Variance1.096385634 × 1010
MonotonicityNot monotonic
2022-05-09T21:02:42.224413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19798261
 
1.9%
22893201
 
1.9%
19798491
 
1.9%
21302701
 
1.9%
19809541
 
1.9%
20420021
 
1.9%
19802081
 
1.9%
19465811
 
1.9%
19810491
 
1.9%
19659501
 
1.9%
Other values (44)44
81.5%
ValueCountFrequency (%)
19432791
1.9%
19465811
1.9%
19537871
1.9%
19612861
1.9%
19620561
1.9%
19659211
1.9%
19659501
1.9%
19670671
1.9%
19681121
1.9%
19690541
1.9%
ValueCountFrequency (%)
23180971
1.9%
23112121
1.9%
22893201
1.9%
22121651
1.9%
21821161
1.9%
21817951
1.9%
21761221
1.9%
21540021
1.9%
21540011
1.9%
21540001
1.9%

url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size560.0 B
https://www.tvmaze.com/episodes/1979826/sim-for-you-4x18-chanyeols-episode-18
 
1
https://www.tvmaze.com/episodes/2289320/blippi-2020-12-05-blippi-decorates-the-christmas-tree-educational-videos-for-kids
 
1
https://www.tvmaze.com/episodes/1979849/neznyj-redaktor-6x01-cajldfri-ili-rozat-zacem-nuzny-deti-intensivnoe-materinstvo-podrugi
 
1
https://www.tvmaze.com/episodes/2130270/wowcraft-1x52-expac-tations
 
1
https://www.tvmaze.com/episodes/1980954/jachtseizoen-5x06-gaby-blaaser-op-de-vlucht
 
1
Other values (49)
49 

Length

Max length131
Median length90
Mean length81.18518519
Min length55

Characters and Unicode

Total characters4384
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/1979826/sim-for-you-4x18-chanyeols-episode-18
2nd rowhttps://www.tvmaze.com/episodes/1968112/po-sezonu-videodajdzest-seasonvar-6x49-vypusk-303
3rd rowhttps://www.tvmaze.com/episodes/1980956/soul-land-7x03-di133ji
4th rowhttps://www.tvmaze.com/episodes/1962056/heaven-officials-blessing-1x07-scorpion-tailed-snake-shadow
5th rowhttps://www.tvmaze.com/episodes/1972559/the-wolf-1x17-episode-17

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1979826/sim-for-you-4x18-chanyeols-episode-181
 
1.9%
https://www.tvmaze.com/episodes/2289320/blippi-2020-12-05-blippi-decorates-the-christmas-tree-educational-videos-for-kids1
 
1.9%
https://www.tvmaze.com/episodes/1979849/neznyj-redaktor-6x01-cajldfri-ili-rozat-zacem-nuzny-deti-intensivnoe-materinstvo-podrugi1
 
1.9%
https://www.tvmaze.com/episodes/2130270/wowcraft-1x52-expac-tations1
 
1.9%
https://www.tvmaze.com/episodes/1980954/jachtseizoen-5x06-gaby-blaaser-op-de-vlucht1
 
1.9%
https://www.tvmaze.com/episodes/2042002/game-changer-wrestling-2020-12-05-gcw-slime-season1
 
1.9%
https://www.tvmaze.com/episodes/1980208/world-war-two-week-by-week-3x14-december-5-19411
 
1.9%
https://www.tvmaze.com/episodes/1946581/detention-1x01-devil1
 
1.9%
https://www.tvmaze.com/episodes/1981049/detention-1x02-who-am-i1
 
1.9%
https://www.tvmaze.com/episodes/1965950/i-told-sunset-about-you-the-documentary-1x08-all-the-significance1
 
1.9%
Other values (44)44
81.5%

Length

2022-05-09T21:02:42.354771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1979826/sim-for-you-4x18-chanyeols-episode-181
 
1.9%
https://www.tvmaze.com/episodes/2041863/fandom-tour-1x12-stray-kidsui-majimag-chueog-yeohaeng1
 
1.9%
https://www.tvmaze.com/episodes/2154002/oteren-svenn-1x10-badebassenget1
 
1.9%
https://www.tvmaze.com/episodes/1980956/soul-land-7x03-di133ji1
 
1.9%
https://www.tvmaze.com/episodes/1962056/heaven-officials-blessing-1x07-scorpion-tailed-snake-shadow1
 
1.9%
https://www.tvmaze.com/episodes/1972559/the-wolf-1x17-episode-171
 
1.9%
https://www.tvmaze.com/episodes/1972560/the-wolf-1x18-episode-181
 
1.9%
https://www.tvmaze.com/episodes/2113317/klassen-3x16-bursdagstyven1
 
1.9%
https://www.tvmaze.com/episodes/1969218/team-ingebrigtsen-4x01-episode-11
 
1.9%
https://www.tvmaze.com/episodes/1980944/team-ingebrigtsen-4x02-episode-21
 
1.9%
Other values (44)44
81.5%

Most occurring characters

ValueCountFrequency (%)
e371
 
8.5%
-338
 
7.7%
s284
 
6.5%
t281
 
6.4%
/270
 
6.2%
o224
 
5.1%
w193
 
4.4%
i179
 
4.1%
a165
 
3.8%
m145
 
3.3%
Other values (30)1934
44.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2989
68.2%
Decimal Number625
 
14.3%
Other Punctuation432
 
9.9%
Dash Punctuation338
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e371
12.4%
s284
 
9.5%
t281
 
9.4%
o224
 
7.5%
w193
 
6.5%
i179
 
6.0%
a165
 
5.5%
m145
 
4.9%
p144
 
4.8%
n118
 
3.9%
Other values (16)885
29.6%
Decimal Number
ValueCountFrequency (%)
1140
22.4%
096
15.4%
295
15.2%
969
11.0%
546
 
7.4%
341
 
6.6%
740
 
6.4%
836
 
5.8%
633
 
5.3%
429
 
4.6%
Other Punctuation
ValueCountFrequency (%)
/270
62.5%
.108
 
25.0%
:54
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-338
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2989
68.2%
Common1395
31.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e371
12.4%
s284
 
9.5%
t281
 
9.4%
o224
 
7.5%
w193
 
6.5%
i179
 
6.0%
a165
 
5.5%
m145
 
4.9%
p144
 
4.8%
n118
 
3.9%
Other values (16)885
29.6%
Common
ValueCountFrequency (%)
-338
24.2%
/270
19.4%
1140
10.0%
.108
 
7.7%
096
 
6.9%
295
 
6.8%
969
 
4.9%
:54
 
3.9%
546
 
3.3%
341
 
2.9%
Other values (4)138
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e371
 
8.5%
-338
 
7.7%
s284
 
6.5%
t281
 
6.4%
/270
 
6.2%
o224
 
5.1%
w193
 
4.4%
i179
 
4.1%
a165
 
3.8%
m145
 
3.3%
Other values (30)1934
44.1%

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct51
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size560.0 B
Episode 1
 
2
Episode 2
 
2
Episode 5
 
2
Chanyeol's Episode 18
 
1
Episode 41
 
1
Other values (46)
46 

Length

Max length72
Median length42.5
Mean length18.2962963
Min length5

Characters and Unicode

Total characters988
Distinct characters111
Distinct categories8 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)88.9%

Sample

1st rowChanyeol's Episode 18
2nd rowВыпуск 303
3rd row第133集
4th rowScorpion-Tailed Snake Shadow
5th rowEpisode 17

Common Values

ValueCountFrequency (%)
Episode 12
 
3.7%
Episode 22
 
3.7%
Episode 52
 
3.7%
Chanyeol's Episode 181
 
1.9%
Episode 411
 
1.9%
Gaby Blaaser op de vlucht1
 
1.9%
GCW Slime Season1
 
1.9%
December 5, 19411
 
1.9%
Devil1
 
1.9%
Who am I?1
 
1.9%
Other values (41)41
75.9%

Length

2022-05-09T21:02:42.537805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode10
 
5.8%
54
 
2.3%
the4
 
2.3%
on3
 
1.7%
3
 
1.7%
vs3
 
1.7%
23
 
1.7%
water2
 
1.2%
december2
 
1.2%
ufc2
 
1.2%
Other values (129)136
79.1%

Most occurring characters

ValueCountFrequency (%)
118
 
11.9%
e72
 
7.3%
i56
 
5.7%
a50
 
5.1%
s46
 
4.7%
o46
 
4.7%
r40
 
4.0%
t40
 
4.0%
n38
 
3.8%
l26
 
2.6%
Other values (101)456
46.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter638
64.6%
Uppercase Letter159
 
16.1%
Space Separator118
 
11.9%
Decimal Number41
 
4.1%
Other Punctuation17
 
1.7%
Other Letter10
 
1.0%
Dash Punctuation3
 
0.3%
Math Symbol2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e72
 
11.3%
i56
 
8.8%
a50
 
7.8%
s46
 
7.2%
o46
 
7.2%
r40
 
6.3%
t40
 
6.3%
n38
 
6.0%
l26
 
4.1%
h25
 
3.9%
Other values (34)199
31.2%
Uppercase Letter
ValueCountFrequency (%)
E18
 
11.3%
S16
 
10.1%
B12
 
7.5%
C10
 
6.3%
T10
 
6.3%
F8
 
5.0%
H7
 
4.4%
P6
 
3.8%
D6
 
3.8%
I5
 
3.1%
Other values (28)61
38.4%
Decimal Number
ValueCountFrequency (%)
111
26.8%
26
14.6%
34
 
9.8%
54
 
9.8%
03
 
7.3%
73
 
7.3%
43
 
7.3%
93
 
7.3%
83
 
7.3%
61
 
2.4%
Other Letter
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Other Punctuation
ValueCountFrequency (%)
.4
23.5%
,4
23.5%
?3
17.6%
:2
11.8%
'2
11.8%
"2
11.8%
Space Separator
ValueCountFrequency (%)
118
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%
Math Symbol
ValueCountFrequency (%)
|2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin726
73.5%
Common181
 
18.3%
Cyrillic71
 
7.2%
Hangul8
 
0.8%
Han2
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e72
 
9.9%
i56
 
7.7%
a50
 
6.9%
s46
 
6.3%
o46
 
6.3%
r40
 
5.5%
t40
 
5.5%
n38
 
5.2%
l26
 
3.6%
h25
 
3.4%
Other values (38)287
39.5%
Cyrillic
ValueCountFrequency (%)
е6
 
8.5%
н6
 
8.5%
и6
 
8.5%
т5
 
7.0%
с4
 
5.6%
р3
 
4.2%
о3
 
4.2%
а3
 
4.2%
И3
 
4.2%
ы2
 
2.8%
Other values (24)30
42.3%
Common
ValueCountFrequency (%)
118
65.2%
111
 
6.1%
26
 
3.3%
.4
 
2.2%
34
 
2.2%
,4
 
2.2%
54
 
2.2%
03
 
1.7%
73
 
1.7%
43
 
1.7%
Other values (9)21
 
11.6%
Hangul
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII903
91.4%
Cyrillic71
 
7.2%
Hangul8
 
0.8%
None4
 
0.4%
CJK2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118
 
13.1%
e72
 
8.0%
i56
 
6.2%
a50
 
5.5%
s46
 
5.1%
o46
 
5.1%
r40
 
4.4%
t40
 
4.4%
n38
 
4.2%
l26
 
2.9%
Other values (55)371
41.1%
Cyrillic
ValueCountFrequency (%)
е6
 
8.5%
н6
 
8.5%
и6
 
8.5%
т5
 
7.0%
с4
 
5.6%
р3
 
4.2%
о3
 
4.2%
а3
 
4.2%
И3
 
4.2%
ы2
 
2.8%
Other values (24)30
42.3%
None
ValueCountFrequency (%)
å2
50.0%
ø2
50.0%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%
Hangul
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.1481481
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-05-09T21:02:42.702858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35.75
95-th percentile2020
Maximum2020
Range2019
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation723.5439616
Coefficient of variation (CV)2.402618001
Kurtosis2.234552979
Mean301.1481481
Median Absolute Deviation (MAD)0
Skewness2.037909832
Sum16262
Variance523515.8644
MonotonicityNot monotonic
2022-05-09T21:02:42.809611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
130
55.6%
20208
 
14.8%
43
 
5.6%
63
 
5.6%
33
 
5.6%
23
 
5.6%
72
 
3.7%
51
 
1.9%
81
 
1.9%
ValueCountFrequency (%)
130
55.6%
23
 
5.6%
33
 
5.6%
43
 
5.6%
51
 
1.9%
63
 
5.6%
72
 
3.7%
81
 
1.9%
20208
 
14.8%
ValueCountFrequency (%)
20208
 
14.8%
81
 
1.9%
72
 
3.7%
63
 
5.6%
51
 
1.9%
43
 
5.6%
33
 
5.6%
23
 
5.6%
130
55.6%

number
Real number (ℝ≥0)

HIGH CORRELATION

Distinct24
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.40740741
Minimum1
Maximum332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-05-09T21:02:42.937169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.25
median7
Q315.75
95-th percentile51.35
Maximum332
Range331
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation46.29785378
Coefficient of variation (CV)2.515175155
Kurtosis41.40711599
Mean18.40740741
Median Absolute Deviation (MAD)4
Skewness6.146898395
Sum994
Variance2143.491265
MonotonicityNot monotonic
2022-05-09T21:02:43.056008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
35
 
9.3%
15
 
9.3%
54
 
7.4%
64
 
7.4%
44
 
7.4%
24
 
7.4%
103
 
5.6%
83
 
5.6%
183
 
5.6%
73
 
5.6%
Other values (14)16
29.6%
ValueCountFrequency (%)
15
9.3%
24
7.4%
35
9.3%
44
7.4%
54
7.4%
64
7.4%
73
5.6%
83
5.6%
91
 
1.9%
103
5.6%
ValueCountFrequency (%)
3321
 
1.9%
841
 
1.9%
521
 
1.9%
511
 
1.9%
491
 
1.9%
411
 
1.9%
292
3.7%
183
5.6%
171
 
1.9%
162
3.7%

type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size560.0 B
regular
54 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters378
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular54
100.0%

Length

2022-05-09T21:02:43.188299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:02:43.356010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
regular54
100.0%

Most occurring characters

ValueCountFrequency (%)
r108
28.6%
e54
14.3%
g54
14.3%
u54
14.3%
l54
14.3%
a54
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter378
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r108
28.6%
e54
14.3%
g54
14.3%
u54
14.3%
l54
14.3%
a54
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin378
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r108
28.6%
e54
14.3%
g54
14.3%
u54
14.3%
l54
14.3%
a54
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r108
28.6%
e54
14.3%
g54
14.3%
u54
14.3%
l54
14.3%
a54
14.3%

airdate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size560.0 B
2020-12-05
54 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters540
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-05
2nd row2020-12-05
3rd row2020-12-05
4th row2020-12-05
5th row2020-12-05

Common Values

ValueCountFrequency (%)
2020-12-0554
100.0%

Length

2022-05-09T21:02:43.461238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:02:43.554729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-0554
100.0%

Most occurring characters

ValueCountFrequency (%)
2162
30.0%
0162
30.0%
-108
20.0%
154
 
10.0%
554
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number432
80.0%
Dash Punctuation108
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2162
37.5%
0162
37.5%
154
 
12.5%
554
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common540
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2162
30.0%
0162
30.0%
-108
20.0%
154
 
10.0%
554
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2162
30.0%
0162
30.0%
-108
20.0%
154
 
10.0%
554
 
10.0%

airtime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size560.0 B
nan
34 
06:00
11:00
 
2
18:00
 
2
10:00
 
1
Other values (10)
10 

Length

Max length5
Median length3
Mean length3.740740741
Min length3

Characters and Unicode

Total characters202
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)20.4%

Sample

1st row06:00
2nd rownan
3rd row10:00
4th row11:00
5th rownan

Common Values

ValueCountFrequency (%)
nan34
63.0%
06:005
 
9.3%
11:002
 
3.7%
18:002
 
3.7%
10:001
 
1.9%
05:001
 
1.9%
17:001
 
1.9%
18:301
 
1.9%
20:001
 
1.9%
00:151
 
1.9%
Other values (5)5
 
9.3%

Length

2022-05-09T21:02:43.734211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan34
63.0%
06:005
 
9.3%
11:002
 
3.7%
18:002
 
3.7%
10:001
 
1.9%
05:001
 
1.9%
17:001
 
1.9%
18:301
 
1.9%
20:001
 
1.9%
00:151
 
1.9%
Other values (5)5
 
9.3%

Most occurring characters

ValueCountFrequency (%)
n68
33.7%
043
21.3%
a34
16.8%
:20
 
9.9%
114
 
6.9%
66
 
3.0%
55
 
2.5%
24
 
2.0%
83
 
1.5%
32
 
1.0%
Other values (2)3
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter102
50.5%
Decimal Number80
39.6%
Other Punctuation20
 
9.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
043
53.8%
114
 
17.5%
66
 
7.5%
55
 
6.2%
24
 
5.0%
83
 
3.8%
32
 
2.5%
92
 
2.5%
71
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
n68
66.7%
a34
33.3%
Other Punctuation
ValueCountFrequency (%)
:20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin102
50.5%
Common100
49.5%

Most frequent character per script

Common
ValueCountFrequency (%)
043
43.0%
:20
20.0%
114
 
14.0%
66
 
6.0%
55
 
5.0%
24
 
4.0%
83
 
3.0%
32
 
2.0%
92
 
2.0%
71
 
1.0%
Latin
ValueCountFrequency (%)
n68
66.7%
a34
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n68
33.7%
043
21.3%
a34
16.8%
:20
 
9.9%
114
 
6.9%
66
 
3.0%
55
 
2.5%
24
 
2.0%
83
 
1.5%
32
 
1.0%
Other values (2)3
 
1.5%

airstamp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct22
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Memory size560.0 B
2020-12-05T12:00:00+00:00
15 
2020-12-05T11:00:00+00:00
11 
2020-12-05T17:00:00+00:00
2020-12-05T04:00:00+00:00
2020-12-05T05:00:00+00:00
Other values (17)
18 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters1350
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)29.6%

Sample

1st row2020-12-04T21:00:00+00:00
2nd row2020-12-05T00:00:00+00:00
3rd row2020-12-05T02:00:00+00:00
4th row2020-12-05T03:00:00+00:00
5th row2020-12-05T04:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-05T12:00:00+00:0015
27.8%
2020-12-05T11:00:00+00:0011
20.4%
2020-12-05T17:00:00+00:004
 
7.4%
2020-12-05T04:00:00+00:003
 
5.6%
2020-12-05T05:00:00+00:003
 
5.6%
2020-12-05T09:00:00+00:002
 
3.7%
2020-12-05T13:00:00+00:001
 
1.9%
2020-12-06T00:30:00+00:001
 
1.9%
2020-12-05T21:00:00+00:001
 
1.9%
2020-12-05T20:55:00+00:001
 
1.9%
Other values (12)12
22.2%

Length

2022-05-09T21:02:43.901530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-05t12:00:00+00:0015
27.8%
2020-12-05t11:00:00+00:0011
20.4%
2020-12-05t17:00:00+00:004
 
7.4%
2020-12-05t04:00:00+00:003
 
5.6%
2020-12-05t05:00:00+00:003
 
5.6%
2020-12-05t09:00:00+00:002
 
3.7%
2020-12-05t00:00:00+00:001
 
1.9%
2020-12-05t02:00:00+00:001
 
1.9%
2020-12-05t03:00:00+00:001
 
1.9%
2020-12-05t06:00:00+00:001
 
1.9%
Other values (12)12
22.2%

Most occurring characters

ValueCountFrequency (%)
0607
45.0%
2181
 
13.4%
:162
 
12.0%
-108
 
8.0%
1102
 
7.6%
559
 
4.4%
T54
 
4.0%
+54
 
4.0%
75
 
0.4%
35
 
0.4%
Other values (4)13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number972
72.0%
Other Punctuation162
 
12.0%
Dash Punctuation108
 
8.0%
Uppercase Letter54
 
4.0%
Math Symbol54
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0607
62.4%
2181
 
18.6%
1102
 
10.5%
559
 
6.1%
75
 
0.5%
35
 
0.5%
44
 
0.4%
64
 
0.4%
93
 
0.3%
82
 
0.2%
Other Punctuation
ValueCountFrequency (%)
:162
100.0%
Dash Punctuation
ValueCountFrequency (%)
-108
100.0%
Uppercase Letter
ValueCountFrequency (%)
T54
100.0%
Math Symbol
ValueCountFrequency (%)
+54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1296
96.0%
Latin54
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0607
46.8%
2181
 
14.0%
:162
 
12.5%
-108
 
8.3%
1102
 
7.9%
559
 
4.6%
+54
 
4.2%
75
 
0.4%
35
 
0.4%
44
 
0.3%
Other values (3)9
 
0.7%
Latin
ValueCountFrequency (%)
T54
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0607
45.0%
2181
 
13.4%
:162
 
12.0%
-108
 
8.0%
1102
 
7.6%
559
 
4.4%
T54
 
4.0%
+54
 
4.0%
75
 
0.4%
35
 
0.4%
Other values (4)13
 
1.0%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct24
Distinct (%)49.0%
Missing5
Missing (%)9.3%
Infinite0
Infinite (%)0.0%
Mean42.2244898
Minimum2
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-05-09T21:02:44.074011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median24
Q347
95-th percentile120
Maximum300
Range298
Interquartile range (IQR)42

Descriptive statistics

Standard deviation60.38772823
Coefficient of variation (CV)1.430158861
Kurtosis11.87114494
Mean42.2244898
Median Absolute Deviation (MAD)19
Skewness3.276010193
Sum2069
Variance3646.677721
MonotonicityNot monotonic
2022-05-09T21:02:44.214244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
511
20.4%
305
 
9.3%
454
 
7.4%
203
 
5.6%
603
 
5.6%
162
 
3.7%
212
 
3.7%
1202
 
3.7%
252
 
3.7%
231
 
1.9%
Other values (14)14
25.9%
(Missing)5
 
9.3%
ValueCountFrequency (%)
21
 
1.9%
41
 
1.9%
511
20.4%
81
 
1.9%
111
 
1.9%
151
 
1.9%
162
 
3.7%
203
 
5.6%
212
 
3.7%
231
 
1.9%
ValueCountFrequency (%)
3001
 
1.9%
2931
 
1.9%
1202
3.7%
931
 
1.9%
911
 
1.9%
611
 
1.9%
603
5.6%
561
 
1.9%
521
 
1.9%
471
 
1.9%

image
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct17
Distinct (%)100.0%
Missing37
Missing (%)68.5%
Memory size560.0 B
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/717768.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/717768.jpg'}
 
1
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/350/877095.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/350/877095.jpg'}
 
1
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/719058.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/719058.jpg'}
 
1
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/329/823874.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/329/823874.jpg'}
 
1
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/717714.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/717714.jpg'}
 
1
Other values (12)
12 

Length

Max length178
Median length176
Mean length176.1176471
Min length176

Characters and Unicode

Total characters2994
Distinct characters38
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)100.0%

Sample

1st row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/717768.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/717768.jpg'}
2nd row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/717771.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/717771.jpg'}
3rd row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/290/726339.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/290/726339.jpg'}
4th row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/361/903572.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/361/903572.jpg'}
5th row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/286/716829.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/286/716829.jpg'}

Common Values

ValueCountFrequency (%)
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/717768.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/717768.jpg'}1
 
1.9%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/350/877095.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/350/877095.jpg'}1
 
1.9%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/719058.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/719058.jpg'}1
 
1.9%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/329/823874.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/329/823874.jpg'}1
 
1.9%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/717714.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/717714.jpg'}1
 
1.9%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/404/1010040.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/404/1010040.jpg'}1
 
1.9%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/285/713903.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/285/713903.jpg'}1
 
1.9%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/329/823867.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/329/823867.jpg'}1
 
1.9%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/286/716865.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/286/716865.jpg'}1
 
1.9%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/717647.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/717647.jpg'}1
 
1.9%
Other values (7)7
 
13.0%
(Missing)37
68.5%

Length

2022-05-09T21:02:44.406098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
medium17
25.0%
original17
25.0%
https://static.tvmaze.com/uploads/images/original_untouched/286/716829.jpg1
 
1.5%
https://static.tvmaze.com/uploads/images/original_untouched/287/717647.jpg1
 
1.5%
https://static.tvmaze.com/uploads/images/medium_landscape/287/717646.jpg1
 
1.5%
https://static.tvmaze.com/uploads/images/original_untouched/287/717646.jpg1
 
1.5%
https://static.tvmaze.com/uploads/images/medium_landscape/290/725743.jpg1
 
1.5%
https://static.tvmaze.com/uploads/images/original_untouched/290/725743.jpg1
 
1.5%
https://static.tvmaze.com/uploads/images/medium_landscape/286/716829.jpg1
 
1.5%
https://static.tvmaze.com/uploads/images/medium_landscape/361/903572.jpg1
 
1.5%
Other values (26)26
38.2%

Most occurring characters

ValueCountFrequency (%)
/238
 
7.9%
a204
 
6.8%
t187
 
6.2%
m170
 
5.7%
i170
 
5.7%
s153
 
5.1%
e136
 
4.5%
'136
 
4.5%
o119
 
4.0%
p119
 
4.0%
Other values (28)1362
45.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2006
67.0%
Other Punctuation561
 
18.7%
Decimal Number308
 
10.3%
Space Separator51
 
1.7%
Connector Punctuation34
 
1.1%
Close Punctuation17
 
0.6%
Open Punctuation17
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a204
 
10.2%
t187
 
9.3%
m170
 
8.5%
i170
 
8.5%
s153
 
7.6%
e136
 
6.8%
o119
 
5.9%
p119
 
5.9%
g102
 
5.1%
c102
 
5.1%
Other values (9)544
27.1%
Decimal Number
ValueCountFrequency (%)
768
22.1%
242
13.6%
840
13.0%
130
9.7%
326
 
8.4%
026
 
8.4%
624
 
7.8%
920
 
6.5%
418
 
5.8%
514
 
4.5%
Other Punctuation
ValueCountFrequency (%)
/238
42.4%
'136
24.2%
.102
18.2%
:68
 
12.1%
,17
 
3.0%
Space Separator
ValueCountFrequency (%)
51
100.0%
Connector Punctuation
ValueCountFrequency (%)
_34
100.0%
Close Punctuation
ValueCountFrequency (%)
}17
100.0%
Open Punctuation
ValueCountFrequency (%)
{17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2006
67.0%
Common988
33.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/238
24.1%
'136
13.8%
.102
10.3%
:68
 
6.9%
768
 
6.9%
51
 
5.2%
242
 
4.3%
840
 
4.0%
_34
 
3.4%
130
 
3.0%
Other values (9)179
18.1%
Latin
ValueCountFrequency (%)
a204
 
10.2%
t187
 
9.3%
m170
 
8.5%
i170
 
8.5%
s153
 
7.6%
e136
 
6.8%
o119
 
5.9%
p119
 
5.9%
g102
 
5.1%
c102
 
5.1%
Other values (9)544
27.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/238
 
7.9%
a204
 
6.8%
t187
 
6.2%
m170
 
5.7%
i170
 
5.7%
s153
 
5.1%
e136
 
4.5%
'136
 
4.5%
o119
 
4.0%
p119
 
4.0%
Other values (28)1362
45.5%

summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Memory size560.0 B
nan
42 
<p><b>#DangerQuest #AbleToFly(?) #EntranceOfAWindSkill</b></p>
 
1
<p>The Wehrmacht is halted by the Red Army at the gates of Moscow. Not only that, but a Red Army counteroffensive begins pushing the Germans back decisively. The Germans are also beginning to withdraw from their siege of Tobruk in North Africa. Japan, however, is advancing all over the Pacific, sending troop transports into the South China Sea, though it is unclear just whom Japan plans to attack. The Japanese are also- in top secrecy- sending a force of aircraft carriers to soon attack the American Pacific fleet at anchor at Pearl Harbor.</p>
 
1
<p>At Greenwood High School, transfer student Liu Yun-hsiang witnesses a shocking incident at a deserted building and runs afoul of school authorities.</p><p><br /> </p>
 
1
<p>After an initial encounter with Fang Jui-hsin's spirit, Yun-hsiang finds herself in an otherworldly dimension and accepts a life-altering bargain.</p>
 
1
Other values (8)

Length

Max length549
Median length3
Mean length45.2037037
Min length3

Characters and Unicode

Total characters2441
Distinct characters63
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)22.2%

Sample

1st row<p><b>#DangerQuest #AbleToFly(?) #EntranceOfAWindSkill</b></p>
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan42
77.8%
<p><b>#DangerQuest #AbleToFly(?) #EntranceOfAWindSkill</b></p>1
 
1.9%
<p>The Wehrmacht is halted by the Red Army at the gates of Moscow. Not only that, but a Red Army counteroffensive begins pushing the Germans back decisively. The Germans are also beginning to withdraw from their siege of Tobruk in North Africa. Japan, however, is advancing all over the Pacific, sending troop transports into the South China Sea, though it is unclear just whom Japan plans to attack. The Japanese are also- in top secrecy- sending a force of aircraft carriers to soon attack the American Pacific fleet at anchor at Pearl Harbor.</p>1
 
1.9%
<p>At Greenwood High School, transfer student Liu Yun-hsiang witnesses a shocking incident at a deserted building and runs afoul of school authorities.</p><p><br /> </p>1
 
1.9%
<p>After an initial encounter with Fang Jui-hsin's spirit, Yun-hsiang finds herself in an otherworldly dimension and accepts a life-altering bargain.</p>1
 
1.9%
<p>Go behind the scenes with Billkin and PP as the reflect on the intimate moments in the show.  The crew also reveal some of the difficult scenes to film involving road closures and other challenges. </p>1
 
1.9%
<p>Supermom Tregaye Fraser throws the ultimate birthday party for Zaire with a Slider and Brat Bar, a Quick Chili for the hot dogs and a tasty Pimento Cheese. For dessert, indulge in delicious Peanut Butter and Strawberry Shortcake Jars.</p>1
 
1.9%
<p>Off and Boat, explore an abandoned four-story building. Off realizes that there is something even scarier.</p>1
 
1.9%
<p>This is an original story not from the book. The keyword in the story is "a meeting over time".</p>1
 
1.9%
<p>This is it. Their chance to tell the world the truth. Will their message be heard?</p>1
 
1.9%
Other values (3)3
 
5.6%

Length

2022-05-09T21:02:44.567178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan42
 
10.3%
the26
 
6.4%
and11
 
2.7%
a11
 
2.7%
to10
 
2.5%
is8
 
2.0%
with7
 
1.7%
in6
 
1.5%
of6
 
1.5%
an5
 
1.2%
Other values (234)276
67.6%

Most occurring characters

ValueCountFrequency (%)
351
14.4%
n203
 
8.3%
a197
 
8.1%
e197
 
8.1%
t156
 
6.4%
i139
 
5.7%
r136
 
5.6%
o118
 
4.8%
s108
 
4.4%
h94
 
3.9%
Other values (53)742
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1839
75.3%
Space Separator355
 
14.5%
Uppercase Letter113
 
4.6%
Other Punctuation66
 
2.7%
Math Symbol58
 
2.4%
Dash Punctuation8
 
0.3%
Close Punctuation1
 
< 0.1%
Open Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n203
11.0%
a197
10.7%
e197
10.7%
t156
 
8.5%
i139
 
7.6%
r136
 
7.4%
o118
 
6.4%
s108
 
5.9%
h94
 
5.1%
l68
 
3.7%
Other values (15)423
23.0%
Uppercase Letter
ValueCountFrequency (%)
T15
13.3%
S11
 
9.7%
P11
 
9.7%
C11
 
9.7%
A9
 
8.0%
B8
 
7.1%
J6
 
5.3%
G5
 
4.4%
L5
 
4.4%
O4
 
3.5%
Other values (13)28
24.8%
Other Punctuation
ValueCountFrequency (%)
.22
33.3%
,17
25.8%
/15
22.7%
'4
 
6.1%
#3
 
4.5%
"2
 
3.0%
?2
 
3.0%
!1
 
1.5%
Space Separator
ValueCountFrequency (%)
351
98.9%
 4
 
1.1%
Math Symbol
ValueCountFrequency (%)
<29
50.0%
>29
50.0%
Dash Punctuation
ValueCountFrequency (%)
-8
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1952
80.0%
Common489
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n203
 
10.4%
a197
 
10.1%
e197
 
10.1%
t156
 
8.0%
i139
 
7.1%
r136
 
7.0%
o118
 
6.0%
s108
 
5.5%
h94
 
4.8%
l68
 
3.5%
Other values (38)536
27.5%
Common
ValueCountFrequency (%)
351
71.8%
<29
 
5.9%
>29
 
5.9%
.22
 
4.5%
,17
 
3.5%
/15
 
3.1%
-8
 
1.6%
 4
 
0.8%
'4
 
0.8%
#3
 
0.6%
Other values (5)7
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2437
99.8%
None4
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
351
14.4%
n203
 
8.3%
a197
 
8.1%
e197
 
8.1%
t156
 
6.4%
i139
 
5.7%
r136
 
5.6%
o118
 
4.8%
s108
 
4.4%
h94
 
3.9%
Other values (52)738
30.3%
None
ValueCountFrequency (%)
 4
100.0%

_embedded_show_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45677.27778
Minimum1596
Maximum61755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-05-09T21:02:44.737373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1596
5-th percentile7234.5
Q140226.75
median51219.5
Q357032
95-th percentile59545.4
Maximum61755
Range60159
Interquartile range (IQR)16805.25

Descriptive statistics

Standard deviation15397.91065
Coefficient of variation (CV)0.337102196
Kurtosis1.606853885
Mean45677.27778
Median Absolute Deviation (MAD)5812.5
Skewness-1.511461308
Sum2466573
Variance237095652.3
MonotonicityNot monotonic
2022-05-09T21:02:44.955646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5703210
 
18.5%
479122
 
3.7%
318942
 
3.7%
511252
 
3.7%
416481
 
1.9%
615361
 
1.9%
538511
 
1.9%
560671
 
1.9%
579451
 
1.9%
608481
 
1.9%
Other values (32)32
59.3%
ValueCountFrequency (%)
15961
1.9%
40911
1.9%
60971
1.9%
78471
1.9%
196671
1.9%
249631
1.9%
252941
1.9%
306061
1.9%
318942
3.7%
336911
1.9%
ValueCountFrequency (%)
617551
 
1.9%
615361
 
1.9%
608481
 
1.9%
588441
 
1.9%
579561
 
1.9%
579451
 
1.9%
5703210
18.5%
566051
 
1.9%
560671
 
1.9%
559191
 
1.9%

_embedded_show_url
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct42
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Memory size560.0 B
https://www.tvmaze.com/shows/57032/oteren-svenn
10 
https://www.tvmaze.com/shows/47912/the-wolf
 
2
https://www.tvmaze.com/shows/31894/team-ingebrigtsen
 
2
https://www.tvmaze.com/shows/51125/detention
 
2
https://www.tvmaze.com/shows/41648/sim-for-you
 
1
Other values (37)
37 

Length

Max length74
Median length62
Mean length51.03703704
Min length38

Characters and Unicode

Total characters2756
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)70.4%

Sample

1st rowhttps://www.tvmaze.com/shows/41648/sim-for-you
2nd rowhttps://www.tvmaze.com/shows/7847/po-sezonu-videodajdzest-seasonvar
3rd rowhttps://www.tvmaze.com/shows/35551/soul-land
4th rowhttps://www.tvmaze.com/shows/51670/heaven-officials-blessing
5th rowhttps://www.tvmaze.com/shows/47912/the-wolf

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/57032/oteren-svenn10
 
18.5%
https://www.tvmaze.com/shows/47912/the-wolf2
 
3.7%
https://www.tvmaze.com/shows/31894/team-ingebrigtsen2
 
3.7%
https://www.tvmaze.com/shows/51125/detention2
 
3.7%
https://www.tvmaze.com/shows/41648/sim-for-you1
 
1.9%
https://www.tvmaze.com/shows/61536/ano-ko-no-yume-wo-mitan-desu1
 
1.9%
https://www.tvmaze.com/shows/53851/tregayes-way-in-the-kitchen1
 
1.9%
https://www.tvmaze.com/shows/56067/yaar-jigree-kasooti-degree1
 
1.9%
https://www.tvmaze.com/shows/57945/i-like-to-watch1
 
1.9%
https://www.tvmaze.com/shows/60848/blippi1
 
1.9%
Other values (32)32
59.3%

Length

2022-05-09T21:02:45.295366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/57032/oteren-svenn10
 
18.5%
https://www.tvmaze.com/shows/51125/detention2
 
3.7%
https://www.tvmaze.com/shows/47912/the-wolf2
 
3.7%
https://www.tvmaze.com/shows/31894/team-ingebrigtsen2
 
3.7%
https://www.tvmaze.com/shows/53888/fandom-tour1
 
1.9%
https://www.tvmaze.com/shows/33691/melon-music-awards1
 
1.9%
https://www.tvmaze.com/shows/35551/soul-land1
 
1.9%
https://www.tvmaze.com/shows/51670/heaven-officials-blessing1
 
1.9%
https://www.tvmaze.com/shows/55919/klassen1
 
1.9%
https://www.tvmaze.com/shows/50752/stjernestov1
 
1.9%
Other values (32)32
59.3%

Most occurring characters

ValueCountFrequency (%)
/270
 
9.8%
w237
 
8.6%
t226
 
8.2%
s218
 
7.9%
o167
 
6.1%
e164
 
6.0%
h127
 
4.6%
m126
 
4.6%
.108
 
3.9%
a100
 
3.6%
Other values (30)1013
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1958
71.0%
Other Punctuation432
 
15.7%
Decimal Number270
 
9.8%
Dash Punctuation96
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w237
12.1%
t226
11.5%
s218
11.1%
o167
 
8.5%
e164
 
8.4%
h127
 
6.5%
m126
 
6.4%
a100
 
5.1%
v72
 
3.7%
c70
 
3.6%
Other values (16)451
23.0%
Decimal Number
ValueCountFrequency (%)
548
17.8%
331
11.5%
228
10.4%
028
10.4%
126
9.6%
426
9.6%
724
8.9%
622
8.1%
920
7.4%
817
 
6.3%
Other Punctuation
ValueCountFrequency (%)
/270
62.5%
.108
 
25.0%
:54
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1958
71.0%
Common798
29.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w237
12.1%
t226
11.5%
s218
11.1%
o167
 
8.5%
e164
 
8.4%
h127
 
6.5%
m126
 
6.4%
a100
 
5.1%
v72
 
3.7%
c70
 
3.6%
Other values (16)451
23.0%
Common
ValueCountFrequency (%)
/270
33.8%
.108
 
13.5%
-96
 
12.0%
:54
 
6.8%
548
 
6.0%
331
 
3.9%
228
 
3.5%
028
 
3.5%
126
 
3.3%
426
 
3.3%
Other values (4)83
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/270
 
9.8%
w237
 
8.6%
t226
 
8.2%
s218
 
7.9%
o167
 
6.1%
e164
 
6.0%
h127
 
4.6%
m126
 
4.6%
.108
 
3.9%
a100
 
3.6%
Other values (30)1013
36.8%

_embedded_show_name
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct42
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Memory size560.0 B
Oteren Svenn
10 
The Wolf
 
2
Team Ingebrigtsen
 
2
Detention
 
2
Sim for You
 
1
Other values (37)
37 

Length

Max length39
Median length28
Mean length16.2962963
Min length4

Characters and Unicode

Total characters880
Distinct characters78
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)70.4%

Sample

1st rowSim for You
2nd rowПо сезону. Видеодайджест Seasonvar
3rd rowSoul Land
4th rowHeaven Official's Blessing
5th rowThe Wolf

Common Values

ValueCountFrequency (%)
Oteren Svenn10
 
18.5%
The Wolf2
 
3.7%
Team Ingebrigtsen2
 
3.7%
Detention2
 
3.7%
Sim for You1
 
1.9%
Ano ko no Yume wo Mitan Desu1
 
1.9%
Tregaye's Way in the Kitchen1
 
1.9%
Yaar Jigree Kasooti Degree1
 
1.9%
I Like to Watch1
 
1.9%
Blippi1
 
1.9%
Other values (32)32
59.3%

Length

2022-05-09T21:02:45.471396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oteren10
 
6.7%
svenn10
 
6.7%
the5
 
3.3%
ufc2
 
1.3%
lovely2
 
1.3%
i2
 
1.3%
to2
 
1.3%
of2
 
1.3%
week2
 
1.3%
fight2
 
1.3%
Other values (104)111
74.0%

Most occurring characters

ValueCountFrequency (%)
e100
 
11.4%
96
 
10.9%
n64
 
7.3%
t48
 
5.5%
i44
 
5.0%
r42
 
4.8%
o40
 
4.5%
a39
 
4.4%
s33
 
3.8%
l23
 
2.6%
Other values (68)351
39.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter628
71.4%
Uppercase Letter142
 
16.1%
Space Separator96
 
10.9%
Other Punctuation10
 
1.1%
Decimal Number4
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e100
15.9%
n64
 
10.2%
t48
 
7.6%
i44
 
7.0%
r42
 
6.7%
o40
 
6.4%
a39
 
6.2%
s33
 
5.3%
l23
 
3.7%
v17
 
2.7%
Other values (35)178
28.3%
Uppercase Letter
ValueCountFrequency (%)
S21
14.8%
W14
 
9.9%
O13
 
9.2%
T13
 
9.2%
D8
 
5.6%
B8
 
5.6%
C7
 
4.9%
I6
 
4.2%
F6
 
4.2%
Y5
 
3.5%
Other values (16)41
28.9%
Other Punctuation
ValueCountFrequency (%)
.4
40.0%
'4
40.0%
:1
 
10.0%
?1
 
10.0%
Decimal Number
ValueCountFrequency (%)
22
50.0%
02
50.0%
Space Separator
ValueCountFrequency (%)
96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin728
82.7%
Common110
 
12.5%
Cyrillic42
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e100
 
13.7%
n64
 
8.8%
t48
 
6.6%
i44
 
6.0%
r42
 
5.8%
o40
 
5.5%
a39
 
5.4%
s33
 
4.5%
l23
 
3.2%
S21
 
2.9%
Other values (40)274
37.6%
Cyrillic
ValueCountFrequency (%)
е6
14.3%
о5
11.9%
д4
 
9.5%
т3
 
7.1%
й2
 
4.8%
ж2
 
4.8%
н2
 
4.8%
р2
 
4.8%
с2
 
4.8%
а2
 
4.8%
Other values (11)12
28.6%
Common
ValueCountFrequency (%)
96
87.3%
.4
 
3.6%
'4
 
3.6%
22
 
1.8%
02
 
1.8%
:1
 
0.9%
?1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII836
95.0%
Cyrillic42
 
4.8%
None2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e100
 
12.0%
96
 
11.5%
n64
 
7.7%
t48
 
5.7%
i44
 
5.3%
r42
 
5.0%
o40
 
4.8%
a39
 
4.7%
s33
 
3.9%
l23
 
2.8%
Other values (45)307
36.7%
Cyrillic
ValueCountFrequency (%)
е6
14.3%
о5
11.9%
д4
 
9.5%
т3
 
7.1%
й2
 
4.8%
ж2
 
4.8%
н2
 
4.8%
р2
 
4.8%
с2
 
4.8%
а2
 
4.8%
Other values (11)12
28.6%
None
ValueCountFrequency (%)
å1
50.0%
ø1
50.0%

_embedded_show_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size560.0 B
Documentary
17 
Scripted
14 
Animation
Talk Show
Reality
Other values (4)

Length

Max length11
Median length10
Mean length8.944444444
Min length6

Characters and Unicode

Total characters483
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)3.7%

Sample

1st rowReality
2nd rowTalk Show
3rd rowAnimation
4th rowAnimation
5th rowScripted

Common Values

ValueCountFrequency (%)
Documentary17
31.5%
Scripted14
25.9%
Animation6
 
11.1%
Talk Show5
 
9.3%
Reality4
 
7.4%
Sports4
 
7.4%
Variety2
 
3.7%
Award Show1
 
1.9%
Game Show1
 
1.9%

Length

2022-05-09T21:02:45.621142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:02:45.791883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
documentary17
27.9%
scripted14
23.0%
show7
11.5%
animation6
 
9.8%
talk5
 
8.2%
reality4
 
6.6%
sports4
 
6.6%
variety2
 
3.3%
award1
 
1.6%
game1
 
1.6%

Most occurring characters

ValueCountFrequency (%)
t47
 
9.7%
e38
 
7.9%
r38
 
7.9%
a36
 
7.5%
o34
 
7.0%
i32
 
6.6%
c31
 
6.4%
n29
 
6.0%
S25
 
5.2%
m24
 
5.0%
Other values (16)149
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter415
85.9%
Uppercase Letter61
 
12.6%
Space Separator7
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t47
11.3%
e38
9.2%
r38
9.2%
a36
8.7%
o34
8.2%
i32
 
7.7%
c31
 
7.5%
n29
 
7.0%
m24
 
5.8%
y23
 
5.5%
Other values (8)83
20.0%
Uppercase Letter
ValueCountFrequency (%)
S25
41.0%
D17
27.9%
A7
 
11.5%
T5
 
8.2%
R4
 
6.6%
V2
 
3.3%
G1
 
1.6%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin476
98.6%
Common7
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t47
 
9.9%
e38
 
8.0%
r38
 
8.0%
a36
 
7.6%
o34
 
7.1%
i32
 
6.7%
c31
 
6.5%
n29
 
6.1%
S25
 
5.3%
m24
 
5.0%
Other values (15)142
29.8%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t47
 
9.7%
e38
 
7.9%
r38
 
7.9%
a36
 
7.5%
o34
 
7.0%
i32
 
6.6%
c31
 
6.4%
n29
 
6.0%
S25
 
5.2%
m24
 
5.0%
Other values (16)149
30.8%

_embedded_show_language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size560.0 B
Norwegian
17 
English
16 
Chinese
Korean
Russian
Other values (5)

Length

Max length9
Median length8
Mean length7.444444444
Min length4

Characters and Unicode

Total characters402
Distinct characters27
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)5.6%

Sample

1st rowKorean
2nd rowRussian
3rd rowChinese
4th rowChinese
5th rowChinese

Common Values

ValueCountFrequency (%)
Norwegian17
31.5%
English16
29.6%
Chinese6
 
11.1%
Korean4
 
7.4%
Russian3
 
5.6%
Japanese3
 
5.6%
Thai2
 
3.7%
Dutch1
 
1.9%
Panjabi1
 
1.9%
Arabic1
 
1.9%

Length

2022-05-09T21:02:46.030284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:02:46.184678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
norwegian17
31.5%
english16
29.6%
chinese6
 
11.1%
korean4
 
7.4%
russian3
 
5.6%
japanese3
 
5.6%
thai2
 
3.7%
dutch1
 
1.9%
panjabi1
 
1.9%
arabic1
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n50
12.4%
i46
11.4%
e39
9.7%
a35
8.7%
g33
 
8.2%
s31
 
7.7%
h25
 
6.2%
r22
 
5.5%
o21
 
5.2%
N17
 
4.2%
Other values (17)83
20.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter348
86.6%
Uppercase Letter54
 
13.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n50
14.4%
i46
13.2%
e39
11.2%
a35
10.1%
g33
9.5%
s31
8.9%
h25
7.2%
r22
6.3%
o21
6.0%
w17
 
4.9%
Other values (7)29
8.3%
Uppercase Letter
ValueCountFrequency (%)
N17
31.5%
E16
29.6%
C6
 
11.1%
K4
 
7.4%
R3
 
5.6%
J3
 
5.6%
T2
 
3.7%
D1
 
1.9%
P1
 
1.9%
A1
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin402
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n50
12.4%
i46
11.4%
e39
9.7%
a35
8.7%
g33
 
8.2%
s31
 
7.7%
h25
 
6.2%
r22
 
5.5%
o21
 
5.2%
N17
 
4.2%
Other values (17)83
20.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII402
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n50
12.4%
i46
11.4%
e39
9.7%
a35
8.7%
g33
 
8.2%
s31
 
7.7%
h25
 
6.2%
r22
 
5.5%
o21
 
5.2%
N17
 
4.2%
Other values (17)83
20.6%

_embedded_show_genres
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size560.0 B
[]
25 
['Comedy']
['Family', 'Sports']
 
2
['Music']
 
2
['Drama', 'Romance', 'History']
 
2
Other values (19)
20 

Length

Max length51
Median length43
Mean length13.37037037
Min length2

Characters and Unicode

Total characters722
Distinct characters33
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)33.3%

Sample

1st row[]
2nd row[]
3rd row['Action', 'Adventure', 'Anime', 'Fantasy']
4th row['Drama', 'Anime', 'Fantasy', 'Romance']
5th row['Drama', 'Romance', 'History']

Common Values

ValueCountFrequency (%)
[]25
46.3%
['Comedy']3
 
5.6%
['Family', 'Sports']2
 
3.7%
['Music']2
 
3.7%
['Drama', 'Romance', 'History']2
 
3.7%
['Drama', 'Horror', 'Thriller']2
 
3.7%
['Drama', 'Anime', 'Fantasy', 'Romance']1
 
1.9%
['Food']1
 
1.9%
['Action', 'Adventure', 'Fantasy']1
 
1.9%
['Comedy', 'Anime', 'Science-Fiction']1
 
1.9%
Other values (14)14
25.9%

Length

2022-05-09T21:02:46.387830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25
27.8%
drama10
 
11.1%
comedy8
 
8.9%
romance5
 
5.6%
horror4
 
4.4%
anime4
 
4.4%
music4
 
4.4%
history3
 
3.3%
sports3
 
3.3%
family3
 
3.3%
Other values (10)21
23.3%

Most occurring characters

ValueCountFrequency (%)
'130
18.0%
[54
 
7.5%
]54
 
7.5%
r41
 
5.7%
a37
 
5.1%
36
 
5.0%
o36
 
5.0%
,36
 
5.0%
e34
 
4.7%
m30
 
4.2%
Other values (23)234
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter343
47.5%
Other Punctuation166
23.0%
Uppercase Letter67
 
9.3%
Open Punctuation54
 
7.5%
Close Punctuation54
 
7.5%
Space Separator36
 
5.0%
Dash Punctuation2
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r41
12.0%
a37
10.8%
o36
10.5%
e34
9.9%
m30
8.7%
i27
7.9%
n24
7.0%
y19
 
5.5%
c18
 
5.2%
t18
 
5.2%
Other values (7)59
17.2%
Uppercase Letter
ValueCountFrequency (%)
D10
14.9%
A10
14.9%
F10
14.9%
C10
14.9%
H7
10.4%
M5
7.5%
R5
7.5%
S5
7.5%
T4
 
6.0%
W1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
'130
78.3%
,36
 
21.7%
Open Punctuation
ValueCountFrequency (%)
[54
100.0%
Close Punctuation
ValueCountFrequency (%)
]54
100.0%
Space Separator
ValueCountFrequency (%)
36
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin410
56.8%
Common312
43.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r41
 
10.0%
a37
 
9.0%
o36
 
8.8%
e34
 
8.3%
m30
 
7.3%
i27
 
6.6%
n24
 
5.9%
y19
 
4.6%
c18
 
4.4%
t18
 
4.4%
Other values (17)126
30.7%
Common
ValueCountFrequency (%)
'130
41.7%
[54
17.3%
]54
17.3%
36
 
11.5%
,36
 
11.5%
-2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII722
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'130
18.0%
[54
 
7.5%
]54
 
7.5%
r41
 
5.7%
a37
 
5.1%
36
 
5.0%
o36
 
5.0%
,36
 
5.0%
e34
 
4.7%
m30
 
4.2%
Other values (23)234
32.4%

_embedded_show_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size560.0 B
Running
27 
To Be Determined
15 
Ended
12 

Length

Max length16
Median length11.5
Mean length9.055555556
Min length5

Characters and Unicode

Total characters489
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowRunning
3rd rowRunning
4th rowRunning
5th rowEnded

Common Values

ValueCountFrequency (%)
Running27
50.0%
To Be Determined15
27.8%
Ended12
22.2%

Length

2022-05-09T21:02:46.541254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:02:46.661293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
running27
32.1%
to15
17.9%
be15
17.9%
determined15
17.9%
ended12
14.3%

Most occurring characters

ValueCountFrequency (%)
n108
22.1%
e72
14.7%
i42
 
8.6%
d39
 
8.0%
30
 
6.1%
R27
 
5.5%
u27
 
5.5%
g27
 
5.5%
T15
 
3.1%
o15
 
3.1%
Other values (6)87
17.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter375
76.7%
Uppercase Letter84
 
17.2%
Space Separator30
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n108
28.8%
e72
19.2%
i42
 
11.2%
d39
 
10.4%
u27
 
7.2%
g27
 
7.2%
o15
 
4.0%
t15
 
4.0%
r15
 
4.0%
m15
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
R27
32.1%
T15
17.9%
B15
17.9%
D15
17.9%
E12
14.3%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin459
93.9%
Common30
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n108
23.5%
e72
15.7%
i42
 
9.2%
d39
 
8.5%
R27
 
5.9%
u27
 
5.9%
g27
 
5.9%
T15
 
3.3%
o15
 
3.3%
B15
 
3.3%
Other values (5)72
15.7%
Common
ValueCountFrequency (%)
30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII489
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n108
22.1%
e72
14.7%
i42
 
8.6%
d39
 
8.0%
30
 
6.1%
R27
 
5.5%
u27
 
5.5%
g27
 
5.5%
T15
 
3.1%
o15
 
3.1%
Other values (6)87
17.8%

_embedded_show_runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct15
Distinct (%)50.0%
Missing24
Missing (%)44.4%
Infinite0
Infinite (%)0.0%
Mean47.8
Minimum2
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-05-09T21:02:46.784513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.9
Q120
median30
Q356.25
95-th percentile120
Maximum300
Range298
Interquartile range (IQR)36.25

Descriptive statistics

Standard deviation58.24466825
Coefficient of variation (CV)1.218507704
Kurtosis11.96444036
Mean47.8
Median Absolute Deviation (MAD)15
Skewness3.11770002
Sum1434
Variance3392.441379
MonotonicityNot monotonic
2022-05-09T21:02:46.888817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
305
 
9.3%
204
 
7.4%
453
 
5.6%
1203
 
5.6%
603
 
5.6%
252
 
3.7%
152
 
3.7%
161
 
1.9%
91
 
1.9%
3001
 
1.9%
Other values (5)5
 
9.3%
(Missing)24
44.4%
ValueCountFrequency (%)
21
 
1.9%
31
 
1.9%
51
 
1.9%
91
 
1.9%
152
 
3.7%
161
 
1.9%
204
7.4%
241
 
1.9%
252
 
3.7%
305
9.3%
ValueCountFrequency (%)
3001
 
1.9%
1203
5.6%
901
 
1.9%
603
5.6%
453
5.6%
305
9.3%
252
 
3.7%
241
 
1.9%
204
7.4%
161
 
1.9%

_embedded_show_averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct28
Distinct (%)57.1%
Missing5
Missing (%)9.3%
Infinite0
Infinite (%)0.0%
Mean37.79591837
Minimum2
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-05-09T21:02:47.077088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median21
Q347
95-th percentile116.2
Maximum300
Range298
Interquartile range (IQR)42

Descriptive statistics

Standard deviation52.18515897
Coefficient of variation (CV)1.38070885
Kurtosis13.90709078
Mean37.79591837
Median Absolute Deviation (MAD)16
Skewness3.376741438
Sum1852
Variance2723.290816
MonotonicityNot monotonic
2022-05-09T21:02:47.241879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
511
20.4%
304
 
7.4%
512
 
3.7%
202
 
3.7%
252
 
3.7%
452
 
3.7%
472
 
3.7%
172
 
3.7%
212
 
3.7%
92
 
3.7%
Other values (18)18
33.3%
(Missing)5
 
9.3%
ValueCountFrequency (%)
21
 
1.9%
31
 
1.9%
511
20.4%
92
 
3.7%
101
 
1.9%
131
 
1.9%
151
 
1.9%
161
 
1.9%
172
 
3.7%
202
 
3.7%
ValueCountFrequency (%)
3001
1.9%
1901
1.9%
1291
1.9%
971
1.9%
881
1.9%
741
1.9%
601
1.9%
581
1.9%
561
1.9%
512
3.7%

_embedded_show_premiered
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size560.0 B
2020-12-05
13 
2020-10-24
 
2
2020-11-14
 
2
2020-11-19
 
2
2016-03-17
 
2
Other values (31)
33 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters540
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)53.7%

Sample

1st row2019-03-25
2nd row2015-02-13
3rd row2018-01-13
4th row2020-10-31
5th row2020-11-19

Common Values

ValueCountFrequency (%)
2020-12-0513
24.1%
2020-10-242
 
3.7%
2020-11-142
 
3.7%
2020-11-192
 
3.7%
2016-03-172
 
3.7%
2020-11-072
 
3.7%
2020-10-032
 
3.7%
2019-03-251
 
1.9%
2019-09-011
 
1.9%
2018-09-151
 
1.9%
Other values (26)26
48.1%

Length

2022-05-09T21:02:47.398714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-0513
24.1%
2020-11-142
 
3.7%
2020-11-192
 
3.7%
2016-03-172
 
3.7%
2020-11-072
 
3.7%
2020-10-032
 
3.7%
2020-10-242
 
3.7%
2009-12-191
 
1.9%
2017-10-171
 
1.9%
2012-01-021
 
1.9%
Other values (26)26
48.1%

Most occurring characters

ValueCountFrequency (%)
0143
26.5%
2113
20.9%
-108
20.0%
194
17.4%
519
 
3.5%
918
 
3.3%
312
 
2.2%
712
 
2.2%
68
 
1.5%
47
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number432
80.0%
Dash Punctuation108
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0143
33.1%
2113
26.2%
194
21.8%
519
 
4.4%
918
 
4.2%
312
 
2.8%
712
 
2.8%
68
 
1.9%
47
 
1.6%
86
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
-108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common540
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0143
26.5%
2113
20.9%
-108
20.0%
194
17.4%
519
 
3.5%
918
 
3.3%
312
 
2.2%
712
 
2.2%
68
 
1.5%
47
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0143
26.5%
2113
20.9%
-108
20.0%
194
17.4%
519
 
3.5%
918
 
3.3%
312
 
2.2%
712
 
2.2%
68
 
1.5%
47
 
1.3%

_embedded_show_ended
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size560.0 B
nan
42 
2020-12-19
 
3
2021-01-04
 
2
2020-12-26
 
2
2020-12-24
 
1
Other values (4)
 
4

Length

Max length10
Median length3
Mean length4.555555556
Min length3

Characters and Unicode

Total characters246
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)9.3%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th row2021-01-04

Common Values

ValueCountFrequency (%)
nan42
77.8%
2020-12-193
 
5.6%
2021-01-042
 
3.7%
2020-12-262
 
3.7%
2020-12-241
 
1.9%
2020-12-051
 
1.9%
2021-01-021
 
1.9%
2021-06-261
 
1.9%
2021-01-091
 
1.9%

Length

2022-05-09T21:02:47.537207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:02:47.692169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan42
77.8%
2020-12-193
 
5.6%
2021-01-042
 
3.7%
2020-12-262
 
3.7%
2020-12-241
 
1.9%
2020-12-051
 
1.9%
2021-01-021
 
1.9%
2021-06-261
 
1.9%
2021-01-091
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n84
34.1%
a42
17.1%
236
14.6%
029
 
11.8%
-24
 
9.8%
119
 
7.7%
94
 
1.6%
64
 
1.6%
43
 
1.2%
51
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter126
51.2%
Decimal Number96
39.0%
Dash Punctuation24
 
9.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
236
37.5%
029
30.2%
119
19.8%
94
 
4.2%
64
 
4.2%
43
 
3.1%
51
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
n84
66.7%
a42
33.3%
Dash Punctuation
ValueCountFrequency (%)
-24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin126
51.2%
Common120
48.8%

Most frequent character per script

Common
ValueCountFrequency (%)
236
30.0%
029
24.2%
-24
20.0%
119
15.8%
94
 
3.3%
64
 
3.3%
43
 
2.5%
51
 
0.8%
Latin
ValueCountFrequency (%)
n84
66.7%
a42
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n84
34.1%
a42
17.1%
236
14.6%
029
 
11.8%
-24
 
9.8%
119
 
7.7%
94
 
1.6%
64
 
1.6%
43
 
1.2%
51
 
0.4%

_embedded_show_officialSite
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct41
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Memory size560.0 B
https://tv.nrk.no/serie/oteren-svenn
10 
https://www.iqiyi.com/lib/m_213579814.html
 
2
https://tv.nrk.no/serie/team-ingebrigtsen
 
2
https://www.netflix.com/title/81329144
 
2
nan
 
2
Other values (36)
36 

Length

Max length102
Median length72
Mean length43.14814815
Min length3

Characters and Unicode

Total characters2330
Distinct characters70
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)66.7%

Sample

1st rowhttps://www.vlive.tv/video/121637
2nd rowhttp://seasonvar.ru/serial-11488-Po_sezonu_Videodajdzhest_Seasonvar.html
3rd rowhttps://v.qq.com/detail/m/m441e3rjq9kwpsc.html
4th rowhttps://www.bilibili.com/tgcf
5th rowhttps://www.iqiyi.com/lib/m_213579814.html

Common Values

ValueCountFrequency (%)
https://tv.nrk.no/serie/oteren-svenn10
 
18.5%
https://www.iqiyi.com/lib/m_213579814.html2
 
3.7%
https://tv.nrk.no/serie/team-ingebrigtsen2
 
3.7%
https://www.netflix.com/title/813291442
 
3.7%
nan2
 
3.7%
https://www.vlive.tv/video/1216371
 
1.9%
https://roosterteeth.com/series/rwby1
 
1.9%
https://www.discoveryplus.com/show/tregayes-way-in-the-kitchen1
 
1.9%
https://m.youtube.com/playlist?list=PLzpGYTdIrMWnRdP7POYwV4Nt-WncCRKwB1
 
1.9%
https://www.youtube.com/playlist?list=PLvahqwMqN4M2o2ZzY6Y8a626Lf286LdVl1
 
1.9%
Other values (31)31
57.4%

Length

2022-05-09T21:02:47.837950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://tv.nrk.no/serie/oteren-svenn10
 
18.5%
https://tv.nrk.no/serie/team-ingebrigtsen2
 
3.7%
https://www.netflix.com/title/813291442
 
3.7%
nan2
 
3.7%
https://www.iqiyi.com/lib/m_213579814.html2
 
3.7%
https://tv.kakao.com/channel/3658620/cliplink/415324903?metaobjecttype=channel1
 
1.9%
https://v.qq.com/detail/m/m441e3rjq9kwpsc.html1
 
1.9%
https://www.bilibili.com/tgcf1
 
1.9%
https://tv.nrk.no/serie/klassen1
 
1.9%
https://tv.nrk.no/serie/stjernestoev1
 
1.9%
Other values (31)31
57.4%

Most occurring characters

ValueCountFrequency (%)
t212
 
9.1%
/204
 
8.8%
e158
 
6.8%
s141
 
6.1%
o111
 
4.8%
n110
 
4.7%
.105
 
4.5%
i92
 
3.9%
w92
 
3.9%
r91
 
3.9%
Other values (60)1014
43.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1671
71.7%
Other Punctuation376
 
16.1%
Decimal Number139
 
6.0%
Uppercase Letter89
 
3.8%
Dash Punctuation39
 
1.7%
Math Symbol9
 
0.4%
Connector Punctuation7
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t212
12.7%
e158
 
9.5%
s141
 
8.4%
o111
 
6.6%
n110
 
6.6%
i92
 
5.5%
w92
 
5.5%
r91
 
5.4%
h85
 
5.1%
p83
 
5.0%
Other values (16)496
29.7%
Uppercase Letter
ValueCountFrequency (%)
P11
 
12.4%
L6
 
6.7%
W6
 
6.7%
C5
 
5.6%
A5
 
5.6%
M5
 
5.6%
T5
 
5.6%
Y5
 
5.6%
B4
 
4.5%
V4
 
4.5%
Other values (15)33
37.1%
Decimal Number
ValueCountFrequency (%)
120
14.4%
420
14.4%
218
12.9%
016
11.5%
315
10.8%
813
9.4%
610
7.2%
99
6.5%
79
6.5%
59
6.5%
Other Punctuation
ValueCountFrequency (%)
/204
54.3%
.105
27.9%
:52
 
13.8%
?6
 
1.6%
%6
 
1.6%
&3
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
-39
100.0%
Math Symbol
ValueCountFrequency (%)
=9
100.0%
Connector Punctuation
ValueCountFrequency (%)
_7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1760
75.5%
Common570
 
24.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t212
 
12.0%
e158
 
9.0%
s141
 
8.0%
o111
 
6.3%
n110
 
6.2%
i92
 
5.2%
w92
 
5.2%
r91
 
5.2%
h85
 
4.8%
p83
 
4.7%
Other values (41)585
33.2%
Common
ValueCountFrequency (%)
/204
35.8%
.105
18.4%
:52
 
9.1%
-39
 
6.8%
120
 
3.5%
420
 
3.5%
218
 
3.2%
016
 
2.8%
315
 
2.6%
813
 
2.3%
Other values (9)68
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t212
 
9.1%
/204
 
8.8%
e158
 
6.8%
s141
 
6.1%
o111
 
4.8%
n110
 
4.7%
.105
 
4.5%
i92
 
3.9%
w92
 
3.9%
r91
 
3.9%
Other values (60)1014
43.5%

_embedded_show_weight
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct30
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.38888889
Minimum0
Maximum93
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-05-09T21:02:47.954757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.3
Q17
median22.5
Q341.5
95-th percentile80.75
Maximum93
Range93
Interquartile range (IQR)34.5

Descriptive statistics

Standard deviation24.93570348
Coefficient of variation (CV)0.8484738425
Kurtosis0.08361043262
Mean29.38888889
Median Absolute Deviation (MAD)15.5
Skewness0.9804981035
Sum1587
Variance621.7893082
MonotonicityNot monotonic
2022-05-09T21:02:48.070327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
711
20.4%
243
 
5.6%
393
 
5.6%
193
 
5.6%
712
 
3.7%
542
 
3.7%
282
 
3.7%
62
 
3.7%
182
 
3.7%
12
 
3.7%
Other values (20)22
40.7%
ValueCountFrequency (%)
01
 
1.9%
12
 
3.7%
31
 
1.9%
62
 
3.7%
711
20.4%
101
 
1.9%
182
 
3.7%
193
 
5.6%
201
 
1.9%
212
 
3.7%
ValueCountFrequency (%)
931
1.9%
891
1.9%
841
1.9%
791
1.9%
712
3.7%
611
1.9%
601
1.9%
571
1.9%
542
3.7%
521
1.9%

_embedded_show_dvdCountry
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size560.0 B
nan
54 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters162
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan54
100.0%

Length

2022-05-09T21:02:48.353943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:02:48.497647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan54
100.0%

Most occurring characters

ValueCountFrequency (%)
n108
66.7%
a54
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter162
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n108
66.7%
a54
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n108
66.7%
a54
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n108
66.7%
a54
33.3%

_embedded_show_summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct41
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Memory size560.0 B
<p>A small otter cub moves under the terrace of the Veie-Rosvoll family. It is named Svenn and quickly becomes a good friend.</p>
10 
<p><b>Detention</b> starts at Greenwood High School in the 1990s. Yunxiang Liu, a transfer student, steps into the forbidden area on the campus by accident, where she encounters the ghost of Ruixin Fang. Fang later unveils the hidden history and trauma over the past 30 years, and how a group of young students and teachers were persecuted as they fought for freedom in the era of censorship. Their stories keep coming back to the school like haunting nightmares, waiting to be told and revealed.  </p>
 
2
<p>The story happens at the end of the Tang Dynasty, when Zhu Wen usurps the throne and establishes the Later Liang Dynasty, and he's known as Emperor Taizu. Ma Zhai Xing (Li Qin) is the daughter of an official and as a child, she befriends a young boy (Darren Wang) who lives in the mountain. One day when he saves a wolf, he accidentally falls over the cliff and is rescued by Zhu Wen. The authoritative figure adopts him as a godson and gives him the title Bo Wang. Ten years later, he saves the female lead per chance and finds her courage and intelligence resonant, and she likes that while he's in a position of power, he still has humility and kindness. She encourages him to fight for justice and he begins that journey by helping the people, stopping throne fights, etc. Even when they have conflicts, they will face those frankly. As they overcome obstacles and fight for justice, feelings deepen and they are able to reap their own happiness by each other's side.</p>
 
2
<p>A documentary series about the running family Ingebrigtsen.</p>
 
2
nan
 
2
Other values (36)
36 

Length

Max length1129
Median length445.5
Mean length318.8148148
Min length3

Characters and Unicode

Total characters17216
Distinct characters80
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)66.7%

Sample

1st row<p><b>Sim for You</b> is a reality series that chronicles each EXO member's life and reveal stories from their everyday life as a series.</p>
2nd row<p>Weekly videodaydzhest on site seasonvar.ru and creative team viruseproject.tv. In ten minutes, we talk about the most important events of the past week: look down on the set is not yet published projects, sharing the secrets of private life actors consider the prospects for the development of genres and discuss news TV industry! In videodaydzheste you will find only reliable information from Russian and foreign publications, as well as take part in choosing the best show of the month! Our weekly news videodaydzhest will suit every viewer, so gather good company with family and friends, as well as stock up on popcorn - these ten minutes you shock, delight and inform the latest news about your favorite TV projects!</p>
3rd row<p>Tang San spent his life in the Tang Outer Sect, dedicated to the creation and mastery of hidden weapons. Once he stole the secret lore of the Inner Sect to reach the pinnacle of his art, his only way out was death. But after throwing himself off the deadly Hell's Peak he was reborn in a different world, the world of Douluo Dalu, a world where every person has a spirit of their own, and those with powerful spirits can practice their spirit power to rise and become Spirit Masters.<br /><br />The spirit that awakens within Tang San is Blue Silver Grass, a useless spirit. Can he overcome the difficulties to reach the high ranks of Spirit Masters and bring the glory of the Tang Sect into this new world?</p>
4th row<p>"Have you heard? The rubbish Heaven Official is having an affair with the ghost realm's number one bigshot!"</p><p>Eight hundred years ago, Xie Lian was the Crown Prince of the Xian Le kingdom; one who was beloved by his citizens and the darling of the world. Unsurprisingly, he ascended to the Heavens at a very young age. Now, eight hundred years later, Xie Lian ascends to the Heavens for the third time as the laughing stock of all three realms. On his first task as a god, he meets a mysterious demon who rules the ghosts and terrifies the Heavens... yet unbeknownst to Xie Lian, this demon king has been paying attention to him for a very, very long time.</p>
5th row<p>The story happens at the end of the Tang Dynasty, when Zhu Wen usurps the throne and establishes the Later Liang Dynasty, and he's known as Emperor Taizu. Ma Zhai Xing (Li Qin) is the daughter of an official and as a child, she befriends a young boy (Darren Wang) who lives in the mountain. One day when he saves a wolf, he accidentally falls over the cliff and is rescued by Zhu Wen. The authoritative figure adopts him as a godson and gives him the title Bo Wang. Ten years later, he saves the female lead per chance and finds her courage and intelligence resonant, and she likes that while he's in a position of power, he still has humility and kindness. She encourages him to fight for justice and he begins that journey by helping the people, stopping throne fights, etc. Even when they have conflicts, they will face those frankly. As they overcome obstacles and fight for justice, feelings deepen and they are able to reap their own happiness by each other's side.</p>

Common Values

ValueCountFrequency (%)
<p>A small otter cub moves under the terrace of the Veie-Rosvoll family. It is named Svenn and quickly becomes a good friend.</p>10
 
18.5%
<p><b>Detention</b> starts at Greenwood High School in the 1990s. Yunxiang Liu, a transfer student, steps into the forbidden area on the campus by accident, where she encounters the ghost of Ruixin Fang. Fang later unveils the hidden history and trauma over the past 30 years, and how a group of young students and teachers were persecuted as they fought for freedom in the era of censorship. Their stories keep coming back to the school like haunting nightmares, waiting to be told and revealed.  </p>2
 
3.7%
<p>The story happens at the end of the Tang Dynasty, when Zhu Wen usurps the throne and establishes the Later Liang Dynasty, and he's known as Emperor Taizu. Ma Zhai Xing (Li Qin) is the daughter of an official and as a child, she befriends a young boy (Darren Wang) who lives in the mountain. One day when he saves a wolf, he accidentally falls over the cliff and is rescued by Zhu Wen. The authoritative figure adopts him as a godson and gives him the title Bo Wang. Ten years later, he saves the female lead per chance and finds her courage and intelligence resonant, and she likes that while he's in a position of power, he still has humility and kindness. She encourages him to fight for justice and he begins that journey by helping the people, stopping throne fights, etc. Even when they have conflicts, they will face those frankly. As they overcome obstacles and fight for justice, feelings deepen and they are able to reap their own happiness by each other's side.</p>2
 
3.7%
<p>A documentary series about the running family Ingebrigtsen.</p>2
 
3.7%
nan2
 
3.7%
<p>The future-fantasy world of Remnant is filled with ravenous monsters, treacherous terrain, and more villains than you can shake a sniper-scythe at. Fortunately, Beacon Academy is training Huntsman and Huntresses to battle the evils of the world, and Ruby, Weiss, Blake, and Yang are ready for their first day of class.</p>1
 
1.9%
<p>Atlanta-based chef and caterer Tregaye Fraser packs flavor and personality into every recipe, sharing her food with the ones she loves -- family, friends and her two growing boys.</p>1
 
1.9%
<p>The web series revolves around the life of university students.</p>1
 
1.9%
<p><b>I Like to Watch</b> is a 2019 American web series hosted by drag queens Trixie Mattel and Katya Zamolodchikova. The series was created by Fran Tirado, produced by Netflix, and streams on the network's YouTube channel. Produced in a similar format to Mattel and Zamolodchikova's web series <i>UNHhhh</i> and <i>The Trixie &amp; Katya Show</i>, <i>I Like to Watch</i> follows its hosts as they view and react to various Netflix Original Programming.</p>1
 
1.9%
<p>FUN EDUCATIONAL videos for children! Kids will learn colors, learn shapes, learn numbers, learn letters, the alphabet, abc's and so much more with Blippi's nursery rhymes, educational songs, and educational videos! Blippi ties in things children love like Backhoes, Tractors, Planes, Trains, Animals, Boats, Unicorns, Construction Equipment, Firetrucks, Horses, and the list goes on! Incorporating cartoons and animation with real life footage!</p>1
 
1.9%
Other values (31)31
57.4%

Length

2022-05-09T21:02:48.638214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the193
 
6.8%
and115
 
4.1%
of78
 
2.7%
a75
 
2.6%
to59
 
2.1%
in44
 
1.6%
is40
 
1.4%
as26
 
0.9%
for26
 
0.9%
on24
 
0.8%
Other values (1136)2157
76.0%

Most occurring characters

ValueCountFrequency (%)
2777
16.1%
e1596
 
9.3%
t1094
 
6.4%
a1058
 
6.1%
i952
 
5.5%
o949
 
5.5%
n932
 
5.4%
s876
 
5.1%
r799
 
4.6%
h655
 
3.8%
Other values (70)5528
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12884
74.8%
Space Separator2786
 
16.2%
Uppercase Letter639
 
3.7%
Other Punctuation467
 
2.7%
Math Symbol348
 
2.0%
Dash Punctuation41
 
0.2%
Decimal Number32
 
0.2%
Open Punctuation9
 
0.1%
Close Punctuation9
 
0.1%
Initial Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1596
12.4%
t1094
 
8.5%
a1058
 
8.2%
i952
 
7.4%
o949
 
7.4%
n932
 
7.2%
s876
 
6.8%
r799
 
6.2%
h655
 
5.1%
l517
 
4.0%
Other values (17)3456
26.8%
Uppercase Letter
ValueCountFrequency (%)
T68
 
10.6%
S53
 
8.3%
I41
 
6.4%
A39
 
6.1%
C36
 
5.6%
L34
 
5.3%
F34
 
5.3%
H31
 
4.9%
W31
 
4.9%
R25
 
3.9%
Other values (16)247
38.7%
Other Punctuation
ValueCountFrequency (%)
,160
34.3%
.139
29.8%
/89
19.1%
'34
 
7.3%
!17
 
3.6%
"14
 
3.0%
:4
 
0.9%
?4
 
0.9%
3
 
0.6%
;2
 
0.4%
Decimal Number
ValueCountFrequency (%)
910
31.2%
07
21.9%
16
18.8%
34
 
12.5%
23
 
9.4%
41
 
3.1%
71
 
3.1%
Space Separator
ValueCountFrequency (%)
2777
99.7%
 9
 
0.3%
Math Symbol
ValueCountFrequency (%)
>174
50.0%
<174
50.0%
Dash Punctuation
ValueCountFrequency (%)
-34
82.9%
7
 
17.1%
Open Punctuation
ValueCountFrequency (%)
(9
100.0%
Close Punctuation
ValueCountFrequency (%)
)9
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13523
78.5%
Common3693
 
21.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1596
 
11.8%
t1094
 
8.1%
a1058
 
7.8%
i952
 
7.0%
o949
 
7.0%
n932
 
6.9%
s876
 
6.5%
r799
 
5.9%
h655
 
4.8%
l517
 
3.8%
Other values (43)4095
30.3%
Common
ValueCountFrequency (%)
2777
75.2%
>174
 
4.7%
<174
 
4.7%
,160
 
4.3%
.139
 
3.8%
/89
 
2.4%
'34
 
0.9%
-34
 
0.9%
!17
 
0.5%
"14
 
0.4%
Other values (17)81
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII17195
99.9%
Punctuation11
 
0.1%
None10
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2777
16.2%
e1596
 
9.3%
t1094
 
6.4%
a1058
 
6.2%
i952
 
5.5%
o949
 
5.5%
n932
 
5.4%
s876
 
5.1%
r799
 
4.6%
h655
 
3.8%
Other values (65)5507
32.0%
None
ValueCountFrequency (%)
 9
90.0%
æ1
 
10.0%
Punctuation
ValueCountFrequency (%)
7
63.6%
3
27.3%
1
 
9.1%

_embedded_show_updated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct42
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1635696534
Minimum1607104092
Maximum1652080636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-05-09T21:02:48.798766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1607104092
5-th percentile1609702620
Q11629906651
median1637055394
Q31646518966
95-th percentile1651384791
Maximum1652080636
Range44976544
Interquartile range (IQR)16612315.5

Descriptive statistics

Standard deviation12835608.25
Coefficient of variation (CV)0.007847181906
Kurtosis-0.2888947175
Mean1635696534
Median Absolute Deviation (MAD)8525195
Skewness-0.6521393897
Sum8.832761284 × 1010
Variance1.64752839 × 1014
MonotonicityNot monotonic
2022-05-09T21:02:48.934960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
162990665110
 
18.5%
16482170292
 
3.7%
16403814422
 
3.7%
16098872012
 
3.7%
16084990071
 
1.9%
16497926551
 
1.9%
16238296751
 
1.9%
16246469541
 
1.9%
16520294141
 
1.9%
16464889081
 
1.9%
Other values (32)32
59.3%
ValueCountFrequency (%)
16071040921
1.9%
16084990071
1.9%
16093598271
1.9%
16098872012
3.7%
16114368421
1.9%
16238295291
1.9%
16238296751
1.9%
16246469541
1.9%
16254352901
1.9%
16275111981
1.9%
ValueCountFrequency (%)
16520806361
1.9%
16520294141
1.9%
16517620441
1.9%
16511816551
1.9%
16509836761
1.9%
16509088001
1.9%
16508602721
1.9%
16507118491
1.9%
16500169181
1.9%
16497926551
1.9%

_links_self_href
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size560.0 B
https://api.tvmaze.com/episodes/1977902
 
1
https://api.tvmaze.com/episodes/2008312
 
1
https://api.tvmaze.com/episodes/2090655
 
1
https://api.tvmaze.com/episodes/2169203
 
1
https://api.tvmaze.com/episodes/2312223
 
1
Other values (49)
49 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters2106
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1977902
2nd rowhttps://api.tvmaze.com/episodes/2015818
3rd rowhttps://api.tvmaze.com/episodes/1964000
4th rowhttps://api.tvmaze.com/episodes/1995405
5th rowhttps://api.tvmaze.com/episodes/2007760

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.9%
https://api.tvmaze.com/episodes/20083121
 
1.9%
https://api.tvmaze.com/episodes/20906551
 
1.9%
https://api.tvmaze.com/episodes/21692031
 
1.9%
https://api.tvmaze.com/episodes/23122231
 
1.9%
https://api.tvmaze.com/episodes/23122241
 
1.9%
https://api.tvmaze.com/episodes/23122251
 
1.9%
https://api.tvmaze.com/episodes/23122261
 
1.9%
https://api.tvmaze.com/episodes/23122271
 
1.9%
https://api.tvmaze.com/episodes/23122281
 
1.9%
Other values (44)44
81.5%

Length

2022-05-09T21:02:49.104835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.9%
https://api.tvmaze.com/episodes/23244321
 
1.9%
https://api.tvmaze.com/episodes/20057621
 
1.9%
https://api.tvmaze.com/episodes/19640001
 
1.9%
https://api.tvmaze.com/episodes/19954051
 
1.9%
https://api.tvmaze.com/episodes/20077601
 
1.9%
https://api.tvmaze.com/episodes/19857891
 
1.9%
https://api.tvmaze.com/episodes/20396221
 
1.9%
https://api.tvmaze.com/episodes/20396231
 
1.9%
https://api.tvmaze.com/episodes/23244271
 
1.9%
Other values (44)44
81.5%

Most occurring characters

ValueCountFrequency (%)
/216
 
10.3%
t162
 
7.7%
p162
 
7.7%
s162
 
7.7%
e162
 
7.7%
a108
 
5.1%
i108
 
5.1%
.108
 
5.1%
m108
 
5.1%
o108
 
5.1%
Other values (16)702
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1350
64.1%
Other Punctuation378
 
17.9%
Decimal Number378
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t162
12.0%
p162
12.0%
s162
12.0%
e162
12.0%
a108
8.0%
i108
8.0%
m108
8.0%
o108
8.0%
h54
 
4.0%
d54
 
4.0%
Other values (3)162
12.0%
Decimal Number
ValueCountFrequency (%)
282
21.7%
952
13.8%
144
11.6%
043
11.4%
330
 
7.9%
827
 
7.1%
427
 
7.1%
726
 
6.9%
624
 
6.3%
523
 
6.1%
Other Punctuation
ValueCountFrequency (%)
/216
57.1%
.108
28.6%
:54
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1350
64.1%
Common756
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/216
28.6%
.108
14.3%
282
 
10.8%
:54
 
7.1%
952
 
6.9%
144
 
5.8%
043
 
5.7%
330
 
4.0%
827
 
3.6%
427
 
3.6%
Other values (3)73
 
9.7%
Latin
ValueCountFrequency (%)
t162
12.0%
p162
12.0%
s162
12.0%
e162
12.0%
a108
8.0%
i108
8.0%
m108
8.0%
o108
8.0%
h54
 
4.0%
d54
 
4.0%
Other values (3)162
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/216
 
10.3%
t162
 
7.7%
p162
 
7.7%
s162
 
7.7%
e162
 
7.7%
a108
 
5.1%
i108
 
5.1%
.108
 
5.1%
m108
 
5.1%
o108
 
5.1%
Other values (16)702
33.3%

Interactions

2022-05-09T21:02:38.154275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:18.689380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:22.977477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:24.972336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:27.801141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:29.983784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:33.273090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:34.792236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:36.349273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:38.832382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:20.091951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:23.586731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:25.710044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:28.532172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:31.335594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:33.501492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:35.261050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:37.006581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:38.944418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:20.471107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:23.693202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:25.895634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:28.691412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:31.602775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:33.638816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:35.394892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:37.128271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:39.029192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:20.936504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:23.797193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:26.044747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:28.843362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:31.873537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:33.754534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:35.507728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:37.226135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:39.142594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:21.209107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:23.918606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:26.189812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:28.973119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:32.122096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:34.039255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:35.622098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:37.389721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:39.398962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:21.664147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:24.398076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:26.500201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:29.272690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:32.439429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:34.275136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:35.840794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:37.614947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:39.502155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:21.897958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:24.507953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:26.753500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:29.409035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:32.615226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:34.449680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:35.950156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:37.839131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:39.597874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:22.163711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:24.668766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:27.173020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:29.690684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:32.879198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:34.556435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:36.063391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:37.948701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:39.732560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:22.524769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:24.811255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:27.489624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:29.822937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:33.075425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:34.672309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:36.228827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:02:38.063875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T21:02:49.200798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T21:02:49.437653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T21:02:49.638097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T21:02:49.858634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T21:02:50.145702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T21:02:40.006846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T21:02:41.098531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T21:02:41.407504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T21:02:41.585078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimeimagesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
01979826https://www.tvmaze.com/episodes/1979826/sim-for-you-4x18-chanyeols-episode-18Chanyeol's Episode 184.018.0regular2020-12-0506:002020-12-04T21:00:00+00:0016.0None<p><b>#DangerQuest #AbleToFly(?) #EntranceOfAWindSkill</b></p>41648https://www.tvmaze.com/shows/41648/sim-for-youSim for YouRealityKorean[]Running16.016.02019-03-25nanhttps://www.vlive.tv/video/12163771.0nan<p><b>Sim for You</b> is a reality series that chronicles each EXO member's life and reveal stories from their everyday life as a series.</p>1.608499e+09https://api.tvmaze.com/episodes/1977902
11968112https://www.tvmaze.com/episodes/1968112/po-sezonu-videodajdzest-seasonvar-6x49-vypusk-303Выпуск 3036.049.0regular2020-12-05nan2020-12-05T00:00:00+00:008.0Nonenan7847https://www.tvmaze.com/shows/7847/po-sezonu-videodajdzest-seasonvarПо сезону. Видеодайджест SeasonvarTalk ShowRussian[]Running9.09.02015-02-13nanhttp://seasonvar.ru/serial-11488-Po_sezonu_Videodajdzhest_Seasonvar.html28.0nan<p>Weekly videodaydzhest on site seasonvar.ru and creative team viruseproject.tv. In ten minutes, we talk about the most important events of the past week: look down on the set is not yet published projects, sharing the secrets of private life actors consider the prospects for the development of genres and discuss news TV industry! In videodaydzheste you will find only reliable information from Russian and foreign publications, as well as take part in choosing the best show of the month! Our weekly news videodaydzhest will suit every viewer, so gather good company with family and friends, as well as stock up on popcorn - these ten minutes you shock, delight and inform the latest news about your favorite TV projects!</p>1.651182e+09https://api.tvmaze.com/episodes/2015818
21980956https://www.tvmaze.com/episodes/1980956/soul-land-7x03-di133ji第133集7.03.0regular2020-12-0510:002020-12-05T02:00:00+00:0020.0Nonenan35551https://www.tvmaze.com/shows/35551/soul-landSoul LandAnimationChinese['Action', 'Adventure', 'Anime', 'Fantasy']Running20.020.02018-01-13nanhttps://v.qq.com/detail/m/m441e3rjq9kwpsc.html89.0nan<p>Tang San spent his life in the Tang Outer Sect, dedicated to the creation and mastery of hidden weapons. Once he stole the secret lore of the Inner Sect to reach the pinnacle of his art, his only way out was death. But after throwing himself off the deadly Hell's Peak he was reborn in a different world, the world of Douluo Dalu, a world where every person has a spirit of their own, and those with powerful spirits can practice their spirit power to rise and become Spirit Masters.<br /><br />The spirit that awakens within Tang San is Blue Silver Grass, a useless spirit. Can he overcome the difficulties to reach the high ranks of Spirit Masters and bring the glory of the Tang Sect into this new world?</p>1.643317e+09https://api.tvmaze.com/episodes/1964000
31962056https://www.tvmaze.com/episodes/1962056/heaven-officials-blessing-1x07-scorpion-tailed-snake-shadowScorpion-Tailed Snake Shadow1.07.0regular2020-12-0511:002020-12-05T03:00:00+00:0025.0Nonenan51670https://www.tvmaze.com/shows/51670/heaven-officials-blessingHeaven Official's BlessingAnimationChinese['Drama', 'Anime', 'Fantasy', 'Romance']Running25.025.02020-10-31nanhttps://www.bilibili.com/tgcf52.0nan<p>"Have you heard? The rubbish Heaven Official is having an affair with the ghost realm's number one bigshot!"</p><p>Eight hundred years ago, Xie Lian was the Crown Prince of the Xian Le kingdom; one who was beloved by his citizens and the darling of the world. Unsurprisingly, he ascended to the Heavens at a very young age. Now, eight hundred years later, Xie Lian ascends to the Heavens for the third time as the laughing stock of all three realms. On his first task as a god, he meets a mysterious demon who rules the ghosts and terrifies the Heavens... yet unbeknownst to Xie Lian, this demon king has been paying attention to him for a very, very long time.</p>1.637712e+09https://api.tvmaze.com/episodes/1995405
41972559https://www.tvmaze.com/episodes/1972559/the-wolf-1x17-episode-17Episode 171.017.0regular2020-12-05nan2020-12-05T04:00:00+00:0045.0Nonenan47912https://www.tvmaze.com/shows/47912/the-wolfThe WolfScriptedChinese['Drama', 'Romance', 'History']Ended45.045.02020-11-192021-01-04https://www.iqiyi.com/lib/m_213579814.html39.0nan<p>The story happens at the end of the Tang Dynasty, when Zhu Wen usurps the throne and establishes the Later Liang Dynasty, and he's known as Emperor Taizu. Ma Zhai Xing (Li Qin) is the daughter of an official and as a child, she befriends a young boy (Darren Wang) who lives in the mountain. One day when he saves a wolf, he accidentally falls over the cliff and is rescued by Zhu Wen. The authoritative figure adopts him as a godson and gives him the title Bo Wang. Ten years later, he saves the female lead per chance and finds her courage and intelligence resonant, and she likes that while he's in a position of power, he still has humility and kindness. She encourages him to fight for justice and he begins that journey by helping the people, stopping throne fights, etc. Even when they have conflicts, they will face those frankly. As they overcome obstacles and fight for justice, feelings deepen and they are able to reap their own happiness by each other's side.</p>1.648217e+09https://api.tvmaze.com/episodes/2007760
51972560https://www.tvmaze.com/episodes/1972560/the-wolf-1x18-episode-18Episode 181.018.0regular2020-12-05nan2020-12-05T04:00:00+00:0045.0Nonenan47912https://www.tvmaze.com/shows/47912/the-wolfThe WolfScriptedChinese['Drama', 'Romance', 'History']Ended45.045.02020-11-192021-01-04https://www.iqiyi.com/lib/m_213579814.html39.0nan<p>The story happens at the end of the Tang Dynasty, when Zhu Wen usurps the throne and establishes the Later Liang Dynasty, and he's known as Emperor Taizu. Ma Zhai Xing (Li Qin) is the daughter of an official and as a child, she befriends a young boy (Darren Wang) who lives in the mountain. One day when he saves a wolf, he accidentally falls over the cliff and is rescued by Zhu Wen. The authoritative figure adopts him as a godson and gives him the title Bo Wang. Ten years later, he saves the female lead per chance and finds her courage and intelligence resonant, and she likes that while he's in a position of power, he still has humility and kindness. She encourages him to fight for justice and he begins that journey by helping the people, stopping throne fights, etc. Even when they have conflicts, they will face those frankly. As they overcome obstacles and fight for justice, feelings deepen and they are able to reap their own happiness by each other's side.</p>1.648217e+09https://api.tvmaze.com/episodes/1985789
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81980944https://www.tvmaze.com/episodes/1980944/team-ingebrigtsen-4x02-episode-2Episode 24.02.0regular2020-12-0506:002020-12-05T05:00:00+00:0061.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/287/717771.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/287/717771.jpg'}nan31894https://www.tvmaze.com/shows/31894/team-ingebrigtsenTeam IngebrigtsenDocumentaryNorwegian['Family', 'Sports']To Be DeterminedNaN47.02016-03-17nanhttps://tv.nrk.no/serie/team-ingebrigtsen23.0nan<p>A documentary series about the running family Ingebrigtsen.</p>1.640381e+09https://api.tvmaze.com/episodes/2324427
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Last rows

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